Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network (2005.10374v4)

Abstract: Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling" in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field. We test the GAN using two datasets, one consisting of radar-measured precipitation from Switzerland, the other of cloud optical thickness derived from the Geostationary Earth Observing Satellite 16 (GOES-16). We find that the GAN can generate realistic, temporally consistent super-resolution sequences for both datasets. The statistical properties of the generated ensemble are analyzed using rank statistics, a method adapted from ensemble weather forecasting; these analyses indicate that the GAN produces close to the correct amount of variability in its outputs. As the GAN generator is fully convolutional, it can be applied after training to input images larger than the images used to train it. It is also able to generate time series much longer than the training sequences, as demonstrated by applying the generator to a three-month dataset of the precipitation radar data. The source code to our GAN is available at https://github.com/jleinonen/downscaling-rnn-gan.

• The paper introduces a novel GAN-RNN framework that generates temporally consistent, high-resolution atmospheric sequences from low-resolution inputs.
• The methodology incorporates ensemble-based metrics like RMSE, MS-SSIM, and rank statistics to validate its ability to capture inherent stochastic variability.
• The findings enhance downscaling techniques in meteorology and open avenues for integrating additional geo-environmental variables for improved predictions.

An Examination of Stochastic Super-Resolution for Atmospheric Data Using GANs

The paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" by Leinonen, Nerini, and Berne presents a sophisticated approach to enhance the spatial resolution of atmospheric fields utilizing Generative Adversarial Networks (GANs). The introduced methodology, which falls under the domain of deep learning, addresses the stochastic nature of atmospheric phenomena, offering a framework for generating high-resolution sequences from low-resolution inputs.

Methodological Framework

The paper leverages conditional GANs combined with Recurrent Neural Networks (RNNs), specifically convolutional GRUs, to generate temporally consistent, high-resolution sequences of atmospheric data. The architecture comprises a generator and discriminator trained adversarially, where the generator's aim is to produce realistic super-resolved sequences that can deceive the discriminator into labeling them as true high-resolution data.

The uniqueness of this work lies in its treatment of the stochastic properties inherent in atmospheric fields, a feature often neglected in traditional super-resolution tasks. By introducing noise into the generation process, the GAN is capable of creating an ensemble of possible high-resolution outcomes for a single low-resolution input, addressing the non-deterministic nature of atmospheric processes.

Empirical Evaluation

Two diverse datasets were utilized to validate the model: the MCH-RZC dataset comprising radar-measured precipitation data from Switzerland, and the cloud optical thickness data from the GOES-16 satellite. Evaluation metrics such as RMSE, MS-SSIM, and LSD were employed to ascertain the quality of image reconstructions. Notably, the inclusion of ensemble variability metrics such as rank statistics provided a comprehensive assessment of the GAN's ability to replicate the variability observed in true atmospheric sequences.

Numerical Findings

The results underscore the efficacy of the proposed GAN-based approach in generating perceptually plausible sequences that exhibit close-to-correct amounts of variability. While traditional metrics struggled to capture the full quality of the reconstructions, ensemble-based evaluations demonstrated that the model could effectively reproduce the stochastic characteristics of atmospheric fields.

Implications and Future Directions

This research has significant implications for advancing stochastic downscaling techniques in meteorology and climatology. By producing high-resolution outputs that account for inherent uncertainties, the approach proposed herein can enhance our understanding of fine-scale atmospheric processes and improve the accuracy of numerical weather predictions.

Looking forward, the methodological framework presents several avenues for further exploration. One promising direction is the integration of auxiliary geo-environmental variables, which could refine super-resolution outputs by incorporating physically relevant constraints. Additionally, extending the GAN to accommodate varying temporal scales holds the potential to improve temporal resolution in atmospheric simulations.

In sum, this paper enriches the discourse on super-resolution applications in atmospheric sciences by presenting a robust stochastic model that provides both practical utility and theoretical insights into the modeling of complex, non-deterministic systems. The successful application of GANs in this context exemplifies the transformative impact of machine learning innovations in geoscientific research.